AI agents run on your audience data at a scale no human team can match. If that data is wrong, they spread the error faster. Three checks to run first.

AI agents are supposed to replace a lot of what human researchers used to do before a purchase decision got made, including pulling sources, spotting patterns, and building audiences.

This isn’t necessarily wrong; it’s that everyone drawing the “agents replace research” conclusion is skipping the actual mechanism. Agents don’t replace the need for good audience data. They run on it, at a scale and speed no human team can match, and whatever quality that data was, good or bad, comes out the other end louder.

To test this, I put some skeptical questions to Mallory Gray, creative director at Skydeo, an audience data company that says it draws on 1.4 trillion data points across more than 320 million people. Skydeo sells exactly the kind of data this argument puts at the center, so it has an obvious stake in the answer. The argument still has to stand on its own, and I think it does.

“A human researcher might look at several sources, identify patterns, form a hypothesis, and build an audience from there,” Gray told me. An agent works across “thousands of behavioral, purchase, interest, and intent signals” simultaneously, continuously revising as new information arrives. Humans still decide what matters and what the brand should do about it. The agent just expands how much raw material can realistically feed that decision.

Source link